Spiking Networks as Non Local Cellular Automata
In this paper, we propose a novel regularization method for spiking neural networks. We note the similarities of a spiking network to a non local cellular automaton and derive a rule for its connections that must be learnt. This rule results in the activations of the network forming a grammar. We also view each state of the spiking network as a node on a hyper graph and show that we can generalise by simply sending the never before seen acivations presented at a novel state or situation, to the next activations that the grammars rule would have them move to. We also demonstrate that hierarchical planning may be achieved by the particular grammar we choose.
https://www.researchgate.net/publication/384809319_Spiking_Networks_as_Non_Local_Cellular_Automata